磁共振图像 (MRI) 是研究脑肿瘤的重要工具，多模态序列为脑肿瘤的不同方面提供了独特的见解。然而，在临床实践中，由于各种因素的影响，常常会出现缺失的情况。这使得获得与脑肿瘤相关的全面可靠的信息变得困难。这项工作的目的是开发一种高精度合成缺失MRI模态的算法，并集中于生成准确的肿瘤相关信息以提供更多数据提出了一种名为 TAM-DAM-GAN 的新型弱监督 MRI 合成模型，该模型集成了肿瘤感知和细节调整机制，以提高肿瘤生成的质量。肿瘤感知机制利用弱标签信息引导网络根据局部结构中的关键信息对图像进行分类，从而迫使生成网络识别并突出局部肿瘤区域的学习。细节调整机制利用鉴别器实时创建像素级的注意力图。然后使用这些映射来修改损失权重，从而调整生成的细节。四个任务（T1-to-T2、T2-to-T1、T1-to-FLAIR 和 FLAIR-to- T1) 进行了评估。 BRATS2015 数据集上的实验表明，所提出的方法在定性和定量测量方面均优于其他方法。以 FLAIR-to-T1 为例，与基线相比，TAM-DAM-GAN 将肿瘤区域的 PSNR 从 18.556 提高到 20.576。此外，与仅使用真实FLAIR数据相比，使用真实FLAIR数据和生成的T1数据可以将肿瘤分割精度提高10%。这一发现将有利于提高不完整多模态MRI中跨模态合成的精度，特别是对于肿瘤区域，从而为临床诊断和科学研究提供更可靠、更全面的数据。© 2024 美国医学物理学家协会。

Magnetic resonance images (MRIs) are a valuable tool in the study of brain tumors, and multimodal sequences provide unique insights into different aspects of brain tumors. However, in clinical practice, missing modalities are often encountered due to various factors. This makes it difficult to obtain comprehensive and reliable information related to brain tumors.The purpose of this work is to develop an algorithm for the synthesis of missing MRI modality with high precision, and to center on generating accurate tumor-related information to offer more data for clinical diagnosis.A novel weakly supervised MRI synthesis model named TAM-DAM-GAN has been proposed, which integrates tumor-aware and detail adjustment mechanisms to enhance the quality of tumor generation. The tumor-aware mechanism leverages weak label information to guide the network to classify images based on crucial information in local structures, thereby compelling the generative network to identify and highlight the learning of local tumor regions. The detail adjustment mechanism utilizes a discriminator to create attention maps at the pixel level in real-time. These maps are then used to modify the loss weight, which in turn adjusts the details that are generated.Generation quality of four tasks (T1-to-T2, T2-to-T1, T1-to-FLAIR, and FLAIR-to-T1) was evaluated. Experiments on the BRATS2015 dataset show that the proposed approach is superior in both qualitative and quantitative measures. Taking FLAIR-to-T1 as an example, TAM-DAM-GAN improves PSNR of tumor region from 18.556 to 20.576 compared to baseline. Also, using real FLAIR data with generated T1 data boosts tumor segmentation accuracy by 10% compared to using only real FLAIR data.This finding will be conducive to enhancing the accuracy of cross-modality synthesis in incomplete multimodal MRI, especially for tumor regions, thereby providing more dependable and comprehensive data for clinical diagnosis and scientific research.© 2024 American Association of Physicists in Medicine.